Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Optical frequency comb source for next generation access networks

The exponential growth of converged telecommunication services and the increasing demands for video rich multimedia applications have triggered the vast development of optical access technology to resolve the capacity bottleneck at metropolitan-access aggregations. To further enhance overall performance, next generation optical access networks will require highly efficient wavelength division multiplexing (WDM) technology beyond the capability of current standard time division multiplexed (TDM) systems. The successful implementation of future-proof WDM access networks depends on advancements in high performance transmission schemes as well as economical and practical electronic/photonic devices. This thesis focuses on an investigation of the use of optical frequency comb sources, and spectrally efficient modulation formats, in high capacity WDM based optical access networks. A novel injected gain switched comb generation technique which deliver simplicity, reliability, and cost effectiveness has been proposed and verified through experimental work. In addition, a detailed characterization of the optical comb source has been undertaken with special attention on the phase noise property of the comb lines. The potential of the injected gain switched comb source is then demonstrated in a digital coherent receiver based long reach WDM access scenario, which intends to facilitate 10 - 40 Gbit/s data delivery per channel . Furthermore, an optical scalar transmission scheme enabling the direct detection of higher order modulation format signals has been proposed and experimentally investigated

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute significantly to the reduction of CO2 emission and enhance resource efficiency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manufacturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource efficiency, a multidisciplinary ICT-energy community needs to be brought together covering devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded systems, efficient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging field and a common framework to strive towards energy-sustainable ICT

Cross-Layer Platform for Dynamic, Energy-Efficient Optical Networks

The design of the next-generation Internet infrastructure is driven by the need to sustain the massive growth in bandwidth demands. Novel, energy-efficient, optical networking technologies and architectures are required to effectively meet the stringent performance requirements with low cost and ultrahigh energy efficiencies. In this thesis, a cross-layer communications platform is proposed to enable greater intelligence and functionality on the physical layer. Providing the optical layer with advanced networking capabilities will facilitate the dynamic management and optimization of optical switching based on performance monitoring measurements and higher-layer attributes. The cross-layer platform aims to create a new framework for networks to incorporate packet-scale measurement subsystems and techniques for monitoring the health of the optical channel. This will allow for quality-of-service- and energy-aware routing schemes, as well as an enhanced awareness of the optical data signals. This thesis first presents the design and development of an optical packet switching fabric. Leveraging a networking test-bed environment to validate networking hypotheses, advanced switching functionalities are demonstrated, including the support for quality-of-service based routing and packet multicasting. The investigated cross-layering is based on emerging optical technologies, enabling packet protection techniques and packet-rate switching fabric reconfiguration. Coupled with fast performance monitoring, the platform will achieve significant performance gains within the endeavor of all-optical switching. Allowing for a more intelligent, programmable optical layer aims to support greater flexibility with respect to bandwidth allocation and potentially a significant reduction in the network's energy consumption. The ultimate deliverable of this work is a high-performance, cross-layer enabled optical network node. The experimental demonstration of an initial prototype creates a dynamic network element with distributed control plane management, featuring fast packet-rate optical switching capabilities and embedded physical-layer performance monitoring modules. The cross-layer box enables an intelligent traffic delivery system that can dynamically manipulate optical switching on a packet-granular scale. With the goal of achieving advanced multi-layer routing and control algorithms, the network node requires an intelligent co-optimization across all the layers. The proposed cross-layer design should drive optical technologies and architectures in an innovative way, in order to fulfill the void between the design of basic photonic devices and the networking protocols that use them. The performance of the entire network -- from the optical components, to the routing algorithms and user applications -- should be optimized in concert. This contribution to the area of cross-layer network design creates an adaptable optical pipe that is extremely flexible and intelligent aware of both the physical optical signals and higher-layer requirements. The impact of this work will be seen in the realization of dynamic, energy-efficient optical communication links in future networking infrastructures

Network Challenges of Novel Sources of Big Data

Networks and networking technologies are the key components of Big Data systems. Modern and future wireless sensor networks (WSN) act as one of the major sources of data for Big Data systems. Wireless networking technologies allow to offload the traffic generated by WSNs to the Internet access points for further delivery to the cloud storage systems. In this thesis we concentrate on the detailed analysis of the following two networking aspects of future Big Data systems: (i) efficient data collection algorithms in WSNs and (ii) wireless data delivery to the Internet access points.The performance evaluation and optimization models developed in the thesis are based on the application of probability theory, theory of stochastic processes, Markov chain theory, stochastic and integral geometries and the queuing theory.The introductory part discusses major components of Big Data systems, identify networking aspects as the subject of interest and formulates the tasks for the thesis. Further, different challenges of Big Data systems are presented in detail with several competitive architectures highlighted. After that, we proceed investigating data collection approaches in modern and future WSNs. We back up the possibility of using the proposed techniques by providing the associated performance evaluation results. We also pay attention to the process of collected data delivery to the Internet backbone access point, and demonstrate that the capacity of conventional cellular systems may not be sufficient for a set of WSN applications including both video monitoring at macro-scale and sensor data delivery from the nano/micro scales. Seeking for a wireless technology for data offloading from WSNs, we study millimeter and terahertz bands. We show there that the interference structure and signal propagation are fundamentally different due to the required use of highly directional antennas, human blocking and molecular absorption. Finally, to characterize the process of collected data transmission from a number of WSNs over the millimeter wave or terahertz backhauls we formulate and solve a queuing system with multiple auto correlated inputs and the service distribution corresponding to the transmission time over a wireless channel with hybrid automatic repeat request mechanism taken into account

Heterogeneous integration of optical wireless communications within next generation networks

Unprecedented traffic growth is expected in future wireless networks and new technologies will be needed to satisfy demand. Optical wireless (OW) communication offers vast unused spectrum and high area spectral efficiency. In this work, optical cells are envisioned as supplementary access points within heterogeneous RF/OW networks. These networks opportunistically offload traffic to optical cells while utilizing the RF cell for highly mobile devices and devices that lack a reliable OW connection. Visible light communication (VLC) is considered as a potential OW technology due to the increasing adoption of solid state lighting for indoor illumination. Results of this work focus on a full system view of RF/OW HetNets with three primary areas of analysis. First, the need for network densication beyond current RF small cell implementations is evaluated. A media independent model is developed and results are presented that provide motivation for the adoption of hyper dense small cells as complementary components within multi-tier networks. Next, the relationships between RF and OW constraints and link characterization parameters are evaluated in order to define methods for fair comparison when user-centric channel selection criteria are used. RF and OW noise and interference characterization techniques are compared and common OW characterization models are demonstrated to show errors in excess of 100x when dominant interferers are present. Finally, dynamic characteristics of hyper dense OW networks are investigated in order to optimize traffic distribution from a network-centric perspective. A Kalman Filter model is presented to predict device motion for improved channel selection and a novel OW range expansion technique is presented that dynamically alters coverage regions of OW cells by 50%. In addition to analytical results, the dissertation describes two tools that have been created for evaluation of RF/OW HetNets. A communication and lighting simulation toolkit has been developed for modeling and evaluation of environments with VLC-enabled luminaires. The toolkit enhances an iterative site based impulse response simulator model to utilize GPU acceleration and achieves 10x speedup over the previous model. A software defined testbed for OW has also been proposed and applied. The testbed implements a VLC link and a heterogeneous RF/VLC connection that demonstrates the RF/OW HetNet concept as proof of concept

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Towards Zero Touch Next Generation Network Management

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques